Quotations
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I have seen the extremely restrictive off-boarding agreement that contains nondisclosure and non-disparagement provisions former OpenAI employees are subject to. It forbids them, for the rest of their lives, from criticizing their former employer. Even acknowledging that the NDA exists is a violation of it.
If a departing employee declines to sign the document, or if they violate it, they can lose all vested equity they earned during their time at the company, which is likely worth millions of dollars.
[...] by default Heroku will spin up multiple dynos in different availability zones. It also has multiple routers in different zones so if one zone should go completely offline, having a second dyno will mean that your app can still serve traffic.
But where the company once limited itself to gathering low-hanging fruit along the lines of “what time is the super bowl,” on Tuesday executives showcased generative AI tools that will someday plan an entire anniversary dinner, or cross-country-move, or trip abroad. A quarter-century into its existence, a company that once proudly served as an entry point to a web that it nourished with traffic and advertising revenue has begun to abstract that all away into an input for its large language models.
If we want LLMs to be less hype and more of a building block for creating useful everyday tools for people, AI companies' shift away from scaling and AGI dreams to acting like regular product companies that focus on cost and customer value proposition is a welcome development.
But unlike the phone system, we can’t separate an LLM’s data from its commands. One of the enormously powerful features of an LLM is that the data affects the code. We want the system to modify its operation when it gets new training data. We want it to change the way it works based on the commands we give it. The fact that LLMs self-modify based on their input data is a feature, not a bug. And it’s the very thing that enables prompt injection.
The MacBook Airs are Apple’s best-selling laptops; the iPad Pros are Apple’s least-selling iPads. I think it’s as simple as this: the current MacBook Airs have the M3, not the M4, because there isn’t yet sufficient supply of M4 chips to satisfy demand for MacBook Airs.
I’m no developer, but I got the AI part working in about an hour.
What took longer was the other stuff: identifying the problem, designing and building the UI, setting up the templating, routes and data architecture.
It reminded me that, in order to capitalise on the potential of AI technologies, we need to really invest in the other stuff too, especially data infrastructure.
It would be ironic, and a huge shame, if AI hype sucked all the investment out of those things.
— Tim Paul
It should be noted that no ethically-trained software engineer would ever consent to write a DestroyBaghdad procedure. Basic professional ethics would instead require him to write a DestroyCity procedure, to which Baghdad could be given as a parameter.
Watching in real time as "slop" becomes a term of art. the way that "spam" became the term for unwanted emails, "slop" is going in the dictionary as the term for unwanted AI generated content
Migrations are not something you can do rarely, or put off, or avoid; not if you are a growing company. Migrations are an ordinary fact of life.
Doing them swiftly, efficiently, and -- most of all -- completely is one of the most critical skills you can develop as a team.
I believe these things: 1. If you use generative tools to produce or modify your images, you have abandoned photointegrity. 2. That’s not always wrong. Sometimes you need an image of a space battle or a Triceratops family or whatever. 3. What is always wrong is using this stuff without disclosing it.
— Tim Bray
I used to have this singular focus on students writing code that they submit, and then I run test cases on the code to determine what their grade is. This is such a narrow view of what it means to be a software engineer, and I just felt that with generative AI, I’ve managed to overcome that restrictive view.
It’s an opportunity for me to assess their learning process of the whole software development [life cycle]—not just code. And I feel like my courses have opened up more and they’re much broader than they used to be. I can make students work on larger and more advanced projects.
AI is the most anthropomorphized technology in history, starting with the name—intelligence—and plenty of other words thrown around the field: learning, neural, vision, attention, bias, hallucination. These references only make sense to us because they are hallmarks of being human. [...]
There is something kind of pathological going on here. One of the most exciting advances in computer science ever achieved, with so many promising uses, and we can't think beyond the most obvious, least useful application? What, because we want to see ourselves in this technology? [...]
Anthropomorphizing AI not only misleads, but suggests we are on equal footing with, even subservient to, this technology, and there's nothing we can do about it.
I'm old enough to remember when the Internet wasn't a group of five websites, each consisting of screenshots of text from the other four.
We collaborate with open-source and commercial model providers to bring their unreleased models to community for preview testing.
Model providers can test their unreleased models anonymously, meaning the models' names will be anonymized. A model is considered unreleased if its weights are neither open, nor available via a public API or service.
— LMSYS
Performance analysis indicates that SQLite spends very little time doing bytecode decoding and dispatch. Most CPU cycles are consumed in walking B-Trees, doing value comparisons, and decoding records - all of which happens in compiled C code. Bytecode dispatch is using less than 3% of the total CPU time, according to my measurements.
So at least in the case of SQLite, compiling all the way down to machine code might provide a performance boost 3% or less. That's not very much, considering the size, complexity, and portability costs involved.
# All the code is wrapped in a main function that gets called at the bottom of the file, so that a truncated partial download doesn't end up executing half a script.
The creator of a model can not ensure that a model is never used to do something harmful – any more so that the developer of a web browser, calculator, or word processor could. Placing liability on the creators of general purpose tools like these mean that, in practice, such tools can not be created at all, except by big businesses with well funded legal teams.
[...] Instead of regulating the development of AI models, the focus should be on regulating their applications, particularly those that pose high risks to public safety and security. Regulate the use of AI in high-risk areas such as healthcare, criminal justice, and critical infrastructure, where the potential for harm is greatest, would ensure accountability for harmful use, whilst allowing for the continued advancement of AI technology.
I've worked out why I don't get much value out of LLMs. The hardest and most time-consuming parts of my job involve distinguishing between ideas that are correct, and ideas that are plausible-sounding but wrong. Current AI is great at the latter type of ideas, and I don't need more of those.
It's very fast to build something that's 90% of a solution. The problem is that the last 10% of building something is usually the hard part which really matters, and with a black box at the center of the product, it feels much more difficult to me to nail that remaining 10%. With vibecheck, most of the time the results to my queries are great; some percentage of the time they aren't. Closing that gap with gen AI feels much more fickle to me than a normal engineering problem. It could be that I'm unfamiliar with it, but I also wonder if some classes of generative AI based products are just doomed to mediocrity as a result.
If you’re auditioning for your job every day, and you’re auditioning against every other brilliant employee there, and you know that at the end of the year, 6% of you are going to get cut no matter what, and at the same time, you have access to unrivaled data on partners, sellers, and competitors, you might be tempted to look at that data to get an edge and keep your job and get to your restricted stock units.
The only difference between screwing around and science is writing it down
— Alex Jason, via Adam Savage
I’ve been at OpenAI for almost a year now. In that time, I’ve trained a lot of generative models. [...] It’s becoming awfully clear to me that these models are truly approximating their datasets to an incredible degree. [...] What this manifests as is – trained on the same dataset for long enough, pretty much every model with enough weights and training time converges to the same point. [...] This is a surprising observation! It implies that model behavior is not determined by architecture, hyperparameters, or optimizer choices. It’s determined by your dataset, nothing else. Everything else is a means to an end in efficiently delivery compute to approximating that dataset.
When I said “Send a text message to Julian Chokkattu,” who’s a friend and fellow AI Pin reviewer over at Wired, I thought I’d be asked what I wanted to tell him. Instead, the device simply said OK and told me it sent the words “Hey Julian, just checking in. How's your day going?” to Chokkattu. I've never said anything like that to him in our years of friendship, but I guess technically the AI Pin did do what I asked.
A bad survey won’t tell you it’s bad. It’s actually really hard to find out that a bad survey is bad — or to tell whether you have written a good or bad set of questions. Bad code will have bugs. A bad interface design will fail a usability test. It’s possible to tell whether you are having a bad user interview right away. Feedback from a bad survey can only come in the form of a second source of information contradicting your analysis of the survey results.
Most seductively, surveys yield responses that are easy to count and counting things feels so certain and objective and truthful.
Even if you are counting lies.
We [Bluesky] took a somewhat novel approach of giving every user their own SQLite database. By removing the Postgres dependency, we made it possible to run a ‘PDS in a box’ [Personal Data Server] without having to worry about managing a database. We didn’t have to worry about things like replicas or failover. For those thinking this is irresponsible: don’t worry, we are backing up all the data on our PDSs!
SQLite worked really well because the PDS – in its ideal form – is a single-tenant system. We owned up to that by having these single tenant SQLite databases.
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone.
The blog post announcing the shutdown was done one day early. The idea was to take the opportunity of the new Pope being announced and Andy Rubin being replaced as head of Android, so that the [Google] Reader news may be drowned out. PR didn't apparently realize that the kinds of people that care about the other two events (especially the Pope) are not the same kind of people that care about Reader, so it didn't work.
I have a child who is also 2e and has been part of the NYC G&T program. We've had a positive experience with the citywide program, specifically with the program at The Anderson School.
— Meta AI bot, answering a question on a forum
In mid-March, we added this line to our system prompt to prevent Claude from thinking it can open URLs:
It cannot open URLs, links, or videos, so if it seems as though the interlocutor is expecting Claude to do so, it clarifies the situation and asks the human to paste the relevant text or image content directly into the conversation.
— Alex Albert, Anthropic